Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, Excel survey exports, and facilitator notes, offers AI-assisted code suggestions and custom-dictionary transcription, and exports shareable visuals while keeping data private. A July 15, 2026 pilot in Kuala Lumpur showed that low-cost, offline gamified activities can boost KAP (knowledge, attitudes, practices) on intestinal parasitic infections across ages (n=90). This post explains how to convert the PLOS pilot (PLOS Neglected Tropical Diseases) into reproducible qualitative evidence using AI-enabled workflows, and how Evidano speeds every step from coded transcripts to co-occurrence networks while keeping data private.
Key Takeaways
This post shows how to turn a July 15, 2026 gamified KAP pilot's mixed data into reproducible qualitative evidence using automated ingestion, AI-assisted coding, and cross-segment analytics.
- The July 15, 2026 pilot (n=90) produced immediate mean gains: knowledge +60.52, attitudes +26.89, and practices +29.32.
- Combine pre/post KAP questionnaires, facilitator notes, and game-score sheets to triangulate why scores changed across children (7–12), adolescents (13–17), and adults (18+).
- Evidano ingests transcripts, Excel survey exports, and facilitator notes, offers AI-assisted code suggestions and custom-dictionary transcription, and exports shareable visuals while keeping data private.
Fast take + source
In brief: A multi-component, gamified health-education pilot published 15 July 2026 improved knowledge by +60.52 points, attitudes by +26.89, and practices by +29.32 across 90 participants (30 children, 30 adolescents, 30 adults).
- Design: 3 half-day, age-tailored sessions with five off-line game stations and illustrated materials.
- Measured: standardised pre/post KAP questionnaire (knowledge 11 items; attitudes 10; practices 11).
- Key result: large, immediate post-intervention gains across domains; equal impact irrespective of baseline scores.
- Full study: PLOS Neglected Tropical Diseases.
Findings snapshot (quick numbers)
| Date | Sample (n) | Key gains (mean point change) | Age groups | Source |
|---|---|---|---|---|
| 15 Jul 2026 | 90 | Knowledge: +60.52; Attitude: +26.89; Practice: +29.32 | Children 7–12 (30); Adolescents 13–17 (30); Adults 18+ (30) | PLOS Neglected Tropical Diseases |
What happened, methods in plain English
This pilot used briefings and age-adapted, offline games (Pictionary, Bingo, matching, charades) to deliver hygiene and prevention messages in a People’s Housing Programme in Klang Valley, and participants completed validated KAP questionnaires immediately before and after the sessions.
- Recruitment: convenience sampling via local residents’ association; inclusion: low-income households in one PHP block.
- Data types generated: pre/post structured questionnaires, facilitator notes, game-score tallies, and community feedback.
- Analysis used paired t-tests and ANOVA to show significant, immediate post-intervention gains.
- Limitations: single-site pilot, immediate post-test only, reliance on self-report (social desirability risk).
What This Means for qualitative analysis of health education pilots
For qualitative researchers
Qualitative researchers should integrate open-ended feedback and facilitator observations with quantitative scores to explain why KAP changed.
This pilot produces mixed qualitative and quantitative artifacts that benefit from integrated coding: open-ended feedback and facilitator observations explain why scores moved.
Use case: extract themes about engagement, local barriers to behaviour change, and suggestions for scaling, then map themes to observed score gains by age.
For program & policy teams
Program and policy teams should treat immediate gains as pedagogic promise and gather qualitative evidence to decide whether to scale.
Immediate gains show pedagogic promise, but qualitative evidence is needed to decide scale.
Decision trigger: if qualitative themes show repeatable, low-cost fixes (better adult facilitation, translated materials), proceed to a cluster trial; otherwise iterate the materials.
Ethics note
The research team should follow local protocols and informed consent procedures for any clinical follow-up such as deworming.
This is research-focused, non-diagnostic work; any clinical follow-up (deworming) must follow local protocols and informed consent procedures as in the original study.
Do more, faster with Evidano
Problem: fragmented inputs → Solution: unified ingestion
Use Evidano to ingest transcripts, Excel survey exports, and facilitator notes into one project so you can run combined thematic and frequency analysis without manual copy-paste.
Pilot outputs include short free-text answers, facilitator memos, and tabulated pre/post scores. Evidano ingests transcripts, Excel survey exports, and facilitator notes in one project so you can run combined thematic plus frequency analysis without manual copy-paste.
Problem: inconsistent coding → Solution: reproducible codebooks
Evidano offers AI-assisted code suggestions and lets teams review and lock code hierarchies to ensure consistent coding across sessions.
Import your validated KAP codebook or use Evidano's AI-assisted code suggestions to auto-code similar responses. Review and lock code hierarchies (theme → subtheme) to ensure consistency across sessions and facilitators.
Problem: segment comparisons are slow → Solution: cross-segment analytics
Evidano provides instant cross-segment comparisons and highlights themes that correlate with the largest pre/post score shifts.
Contrast children, adolescent, and adult themes and quote-rates instantly. Evidano highlights which themes correlate with the largest pre/post score shifts so you can prioritise behavioural levers for adults versus adolescents.
Problem: stakeholder-ready outputs → Solution: visualisations & extracts
Evidano exports co-occurrence networks, hierarchical code trees, word clouds, and clickable quotes for briefings.
Export co-occurrence networks, hierarchical code trees, and word clouds plus clickable quotes for briefings. Generate a short evidence summary (exec brief) and shareable visuals for policy meetings.
Security & governance
Evidano encrypts data and uses proprietary models so your data is not used to train third-party models.
Data is encrypted, and Evidano models are proprietary, your data is not used to train third-party models. This supports sensitive community research where participant privacy matters.
Two-week workflow to reproduce this pilot’s qualitative analysis
This two-week runbook shows how to go from raw files to insight in 11–14 days using automated ingestion, codebook finalisation, and visual exports.
- Day 1–2: Collect and upload pre/post survey exports, facilitator notes, and any audio recordings (transcribe inside Evidano; add custom dictionary for local terms).
- Day 3–5: Auto-suggest codes from a seed list (e.g., 'waste disposal', 'handwashing', 'incentive response'); review and finalize codebook.
- Day 6–8: Run thematic frequency and co-occurrence analyses; tag illustrative quotes and link to participant age groups.
- Day 9–10: Produce cross-segment comparisons (children vs adults vs adolescents) and visual exports (code tree, network, word cloud).
- Day 11–14: Draft a 1‑page executive brief with recommended adaptations and confidence notes; prepare materials for stakeholder review.
FAQ: qualitative analysis of health education pilots
How did the pilot measure KAP and what were the key quantitative results?
The pilot measured KAP with validated pre/post questionnaires and showed immediate mean gains: knowledge +60.52, attitudes +26.89, practices +29.32.
The study used a standardised pre/post KAP questionnaire (knowledge 11 items; attitudes 10; practices 11) and analysed changes with paired t-tests and ANOVA.
What qualitative data were collected and how should researchers use them?
The pilot collected facilitator notes, free-text feedback, and game-score tallies, and researchers should triangulate those artifacts with score shifts to surface engagement drivers.
Use integrated coding to link facilitator observations and open-ended feedback to quantitative score changes, for example mapping adult engagement barriers to smaller or different score patterns.
What are the main limitations to consider when interpreting results?
The main limitations are the single-site design, immediate post-test only, and reliance on self-report which risks social desirability bias.
Because the pilot is single-site with convenience sampling, teams should collect qualitative feasibility data (timing, incentives, space) before scaling decisions.
How can a team compare pre/post themes reliably?
Teams should use a consistent codebook version and anchor codes to pre/post flags to compute theme-level change.
Evidano lets you anchor codes to pre/post flags so you can compute theme-level change and run frequency and co-occurrence shifts by participant ID.
Wrapping up & next steps
The July 15, 2026 gamified KAP pilot demonstrates strong short-term gains and highlights the value of pairing quantitative pre/post metrics with rapid qualitative coding to explain mechanisms and surface scalable fixes.
- For teams evaluating similar interventions (NTDs, WASH, or school health), reproduce the study’s insights faster by combining automated ingestion, reproducible codebooks, and cross-segment visual analytics.
- Want to try it? Try Evidano for free to upload transcripts and survey exports, run thematic and cross-segment analyses, and produce an executive brief in days rather than weeks.
- Next move: run a two-week pilot analysis on one community dataset (n≈30–100) to validate themes, then scale decisions for a cluster or stepped-wedge evaluation.